最新刊期
卷 23 ， 期 6 ， 2019
- 摘要：The LJ-1 01 satellite is a multi-purpose low-orbit scientific experimental satellite with both remote sensing and navigation. In this paper, aiming at the night light remote sensing design and processing of LJ-1 01 satellite, the satellite platform’s payload and platform design are introduced in detail; scientifically oriented experimental objectives, developed a scientific night light imaging mission plan, realized a national luminous map task planning; proposed an in-orbit geometric radiation calibration model for the LJ-1 01 satellite; A data sharing service system supporting WEB and mobile access is developed; for the socio-economic application of luminous remote sensing data, the LJ-1 01 satellite index model of development index and future development index is proposed, and the validity of the model is verified by relevant experiments. The LJ-1 01 satellite will provide technical support for social and economic parameter estimation, major event assessment, fishery monitoring, national security and relative regions.关键词：LJ-1 01 satellite;night light remote sensing;imaging mission planning;geometric radiometric calibration;data sharing service system;LJ-1 01 satellite index
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发布时间：2021-06-07 -
摘要：Synthetic Aperture Radar (SAR) remote sensing has experienced 60 years of development. It has played an important role in earth observation, especially in the fields of global change, resource exploration, environmental monitoring, disaster assessment, urban planning and planetary exploration. Throughout the development process of SAR remote sensing, it has essentially been a process of continuous exploitation and utilization of microwave and electromagnetic wave resources. According to its observation technology and electromagnetic wave resources utilized, SAR remote sensing has experienced four stages: the single-band and single-polarization SAR stage, the multi-band and multi-polarization SAR stage, the polarization and interferometric SAR stage, and the fourth stage or the new stage marked by the emergence of dual/multi-station or constellation observation, high spatio-temporal resolution and wide swath mapping, and 3-D structure imaging capability. Based on the author’s rich research experiences in the field of SAR remote sensing, this paper first briefly summarizes and reviews the characteristics of various stages and the landmark technologies, as well as the typical applications of SAR remote sensing in agriculture, forestry, geology and disaster application fields. Secondly, from the viewpoint of observation technology, data processing and application, the paper expounds the development trend of SAR remote sensing in the new stage, as well as the prospect of combining SAR remote sensing with artificial intelligence and big data techniques. Finally, with a view to the future, the promising research of the lunar-based SAR earth observation platform is introduced. Establishing a Moon-based SAR Earth observation system can help to observe large scale geoscientific phenomena which is difficult to obtain from airborne or spaceborne satellites, and will open up a new direction of SAR earth observation technology and application.关键词：radar remote sensing;four-stage development;retrospect and prospects;moon-based SAR earth observation- 688
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发布时间：2021-06-07 -
摘要：This paper comprehensively and systematically reviews the course of the civil earth observation field (remote sensing industry) in China in the past 20 years, especially the 13-year implementation of the structure adjustment of Space-earth integration and the 10-year development course of the " Major Special Project of High Resolution Earth Observation System” from demonstration to implementation, reveals the basic law of the development of the industry, divides the development of the civil remote sensing industry into four stages for the first time, and divides it into four stages for the first time. This paper elaborates the main characteristics, problems and innovative management practices of each stage, defines for the first time the 10-year course of " Major Special Project of China High Resolution Earth Observation System(CHEOS)” as a development stage, and puts forward that the future development of this industry should be based on the achievements of CHEOS to promote the remote sensing industry to " System Effectiveness”. Following the success of the transformation from experimental application to business service, the new system of Earth Observation in China will be led and promoted again.关键词：space management;major special project of high resolution earth observation system;single satellite application;single satellite service;multi-satellite integrated service;system effectiveness;new earth observation system- 588
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发布时间：2021-06-07 -
摘要：At present, every country in the field of remote sensing is increasing investment, and satellite data resources are increasingly rich. With the development and in-depth application of big data and other cutting-edge technologies, remote sensing has been fully applied at the macro level, and is an important data source for global change and earth system science research. China has become a big remote sensing country in the world. The implementation of high-resolution project marks the civil space remote sensing entering the sub-meter era. However, due to the contradiction between spatial resolution and revisit period, the availability of high-resolution satellite data can not meet some growing application needs. The application of remote sensing in China is still at the macro level, dominated by government departments and users at all levels of government. Remote sensing has not yet been implemented to meet a large number of micro and public specific applications. The emergence and rapid development of UAV remote sensing has played a very good role in promoting the application of remote sensing. UAV remote sensing has high scientific value in the refinement of regional information, with the characteristics of high spatial resolution, high frequency and high cost performance. It can complement the satellite remote sensing ability, alleviate the contradiction between high spatial resolution and time resolution, and on the basis of low cost It realizes the dialectical unity of space and time. At present, the development of UAV remote sensing is still in the primary stage, but it presents some unique application prospects, especially the rapid development of UAV " remote sensing +” application. At the same time, there are many problems in policy, technology and method in the process of development. At the same time of summarizing the future development trend of UAV, this paper puts forward some suggestions on the development of UAV remote sensing from the aspects of policy, technology and application.关键词：UAV;remote sensing application;popular application;remote sensing +;development trend- 801
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发布时间：2021-06-07 -
摘要：The Chinese Academy of Sciences has launched a Chinese remote sensing-based agriculture monitoring system (called CropWatch) in 1998 to provide agriculture information for supporting national food security strategies. CropWatch has provided operational bulletins and has been upgraded to a global agriculture monitoring system, which is unique and uses remote sensing data as the major data source. This study systematically summarizes the development of CropWatch in terms of agricultural monitoring approach/methodology and matrix, system platform, and agricultural information dissemination since 2013. This study emphasizes the innovative agricultural monitoring approach and matrix and pioneering customizable agricultural monitoring system since the establishment of the CropWatch participatory global agricultural monitoring cloud platform (CropWatch-Cloud). A hierarchical approach is adopted by CropWatch, in which a four-tier monitoring strategy, namely, global (sixty-five Crop Monitoring and Reporting Units, MRU), regional (seven Major Production Zones, MPZ), national (31 key countries), and subnational (subdivisions of nine large countries), is developed. CropWatch indicators/matrix can be divided into four categories, namely, CropWatch agroclimatic, arable land use intensity, crop condition, and crop production indicators. Four temporal and spatial resolutions of indicators adapted to each scale are used in CropWatch bulletin. Using the outputs on the four levels, a synthesis of crop condition and production estimates for four major crops is published in the CropWatch bulletin. In situ measurements, local experience, and knowledge are essential to upgrading and improving the robustness of the indicators, methodologies, and algorithms in the CropWatch system. Furthermore, acquiring valuable information outside China, in addition to the global information collection, is challenging. Recently, geotagged data from the crowd or from search engines, such as Google, have revealed interesting scientific trends and have been confirmed as an effective and costless means for data collection, that is, the ability to characterize crop planting dates, or training and validation samples for cropland mapping. External application promotion at home and abroad is emphasized. With the development of CropWatch Cloud, data analysis and reporting are feasible from any location in the world through the Internet. This technology provides unique opportunities to countries requiring work on crop monitoring but currently lack sufficient resources. The innovative approach of the participatory CropWatch-Cloud platform makes this technology an influential agricultural monitoring system. In terms of the originality and innovation of CropWatch, four items are systematically summarized from a methodological perspective. These items are (1) six unique remote sensing-based indicators for an agricultural assessment, (2) advances of early-warning capability, (3) integrated crop condition assessment methods that consider the advantages of six methods, and (4) the innovative crop area estimation method supported by crowd-sourcing data. This study expects the future development of CropWatch. The CropWatch team will leverage CropWatch based on the development of volunteered geographic information acquisition and big Earth data technology to provide agricultural information from the global scale for policy making to the field scale for farm management.关键词：remote sensing;CropWatch;global agriculture monitoring;cloud platform- 672
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发布时间：2021-06-07 - 摘要：The detection of the changes in surface parameters is important to understand the regularity of the Earth’s surface. Traditional data sources, such as in situ stations and field observations, have been extensively used to analyze the spatiotemporal changes in geographic parameters. However, this kind of data only provides information at " point scales,” which cannot comprehensively reflect the spatiotemporal change information of land surfaces. Remote sensing provides a practical means of spatially and continuously obtaining the land surface information at regular intervals. However, the achievable information remains limited by specific spatial and temporal resolutions. Furthermore, results of change detection are typically inconsistent when different remote sensing data are used because of the heterogeneities and seasonal change in land surfaces. This study reviewed the causes and effects of spatial and temporal scales on the change detection of land surface parameters. The methods for reducing the uncertainty of change detection results are summarized. Spatial scale involves the size of the spatial extent and spatial resolution. Similarly, temporal scale partially refers to the length of time range and temporal resolution. The features of the surface landscape are complexity, heterogeneity, and fragmentation. Consequently, the geographic entity exists at a specific spatial and temporal frame. Thus, the completeness of the acquired surface information depends on the spatial and temporal scales of remote sensing data. The geographic elements, which are homogeneous on a scale, may be heterogeneous on another scale. Generally, the remote sensing data at large pixel scales frequently combine the detailed information contained in small pixel scales. Low temporal resolution can result in the inability to capture the rapid changes in land surfaces. For the uncertainty caused by spatial scale, the methods for reducing such kind of effects include multisource remote sensing collaborative observation and inversion, scale conversion, and spatial modeling. For the temporal scale, the methods for reducing such kind of effect are mainly focused on combining multitemporal remote sensing data. The advantages and disadvantages of these methods in practical applications were analyzed in this study. Although many scholars have developed methods for reducing the influence of spatiotemporal scale on the change detection of geographic elements, a universal method is difficult to establish given the contradiction between the spatially and temporally varying characteristics of geographic elements and the nature of remote sensing data. However, these impacts can be minimized by selecting appropriate observation scales and improving the algorithms based on the characteristics of surface parameters. With the diversity of high-quality satellite data and the improvement of the algorithms, the uncertainties of change detection results can be reduced.关键词：spatial and temporal scale;spatial and temporal variation;geographical factors;surface heterogeneity;remote sensing;multi-source data
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发布时间：2021-06-07 - 摘要：In ocean optics, a Volume Scattering Function (VSF) is a fundamental inherent optical property parameter that describes the angular distribution of light scattered from an incident beam, and has considerable significance in studying ocean color remote sensing, underwater light radiation transmission, water environment monitoring and protection, air–sea interaction, and submarine target tracking. Despite its fundamental nature, the variability of the VSF is rarely reported, especially from 0° to 180° (i.e., general angle). This condition is mainly due to the extreme difficulty of performing direct measurements of general-angle VSF. With more than 50 years of development, the measurement technique of general-angle VSF remains in its infancy and is still being explored and improved. This study summarizes the research progress of measurement technology and application of the general-angle VSF of water and discusses the development status quo and the tendency of measurement technology and its application of general-angle VSF of water. On the basis of the measuring principle, the measurement technology of VSF can be divided into " mechanical rotating measurement by a single detector,” " synchronous measurement by array detectors,” and " photographic measurement by a plane array Charge-coupled Device.” The first instruments dedicated to measuring the general-angle VSF of water samples were developed in 1950—1960 and were still used up to this day. The principle of these instruments was principally based on either a light detector or a light source rotating around the sample volume. The angular distribution of scattered light can be measured with the rotation of a single detector or light source (or prism replacing the detector). The angular resolution of these instruments can reach 0.3°, regardless of measurement speed and data synchronization, and these instruments cannot be used in situ. Since the early 1990 s, the second-generation general-angle VSF instruments were built because of the significant advances in the fields of oceanic, optical, and electronics technology. This type of general-angle VSF instruments (referred to as " in situ concept” instruments) uses an array of several detectors that are located in the same plane with a monochromatic light source, which completes a rapid full measurement of VSF within 10 ms, and the angular resolution of these instruments can reach 10°. Some researchers have proposed a new optical approach to measuring the general-angle VSF through image detection. This type of instruments (operates in a laboratory environment) uses a combination of two reflectors and a CCD camera without changing the detector sensitivity and without moving any optical part, thereby allowing the simultaneous measurement of VSF at various angles with a few seconds. The application of direct measurement technology and instrument of general-angle VSF has gradually expanded to the bio-optical model, bubble trace, remote sensing of ocean color, radiative transfer, and upper-ocean heat balance. In the future, the direct measurement technology of general-angle VSF will play an important role in applying oceanic optics and its related field with the increase in application requirements and progress of measurement technology and theoretical methods.关键词：general-angle;Volume Scattering Function(VSF);measurement technique;ocean color remote sensing;water environment monitoring
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发布时间：2021-06-07 -
摘要：Coral reefs are considered a marine ecosystem containing the highest level of biodiversity and primary productivity and are important to the healthy and sustainable development of future human society and marine ecological environment. Coral reefs are the main national land types in the South China Sea, and the state has paid increasing attention to planning, managing, constructing, and protecting coral reefs. All these activities are based on the investigation and measurement of coral reefs. Considering that remote sensing is an effective technique, which has potentials for monitoring the coral reefs in a large scale, remote sensing for the investigation and detection of coral reefs has been conducted by many authors in the past. On this basis, we review the progress of the study on and application of coral reef remote sensing at home and abroad for passive and active remote sensing. Passive remote sensing of coral reefs involves the remote sensing of bottom topography and water depth, substrate classifications and geomorphic units, change detection and stability assessment, and environmental factors. Active remote sensing involves airborne laser bathymetry, shipborne acoustics detection, and microwave remote sensing. The development trends of coral reef landform remote sensing are discussed, and the opportunities and challenges for the remote sensing of national coral reefs are presented. The overall level of our coral reef remote sensing is lagging behind other countries. Some coral reef remote sensing technologies are more advanced at home than abroad, but the depth and breadth of basic applications remain lagging behind those of overseas. Thus, to make coral reef remote sensing suitable in real engineering, our remote sensing experts have been suggested to collaborate with coral reef scholars to strengthen the specific studies on hardware devices, data processing technologies, and basic engineering applications.关键词：ocean;geomorphology;coral reef;classification of remote sensing;shallow-water depth detection- 587
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发布时间：2021-06-07 - 摘要：Thermal Infrared Remote Sensing (TIRS) data from earth observation system have been extensively used in agricultural applications, land survey, drought monitoring, ecology analysis, and many studies on surface processes of land–air interaction. Clouds are an essential obstacle in Remote Sensing (RS) applications and Land Surface Temperature (LST) retrieval from the TIRS data. However, a few studies have investigated the mechanism of LST change under the complicated impacts of clouds, thereby leading to the estimation of LST for cloud-covered pixels to remain unsolved in existing TIRS studies. LST change under cloud cover is mainly governed by the variation in solar radiation that reaches the cloud-covered ground and thermodynamic property of the land surface. This study aims to present an approach to estimating the LST covered by clouds based on the relationship between LST and radiation through the simulation of a surface energy balance model. The quantitative relationship between LST and radiation is estimated using the surface energy balance model and compared with field observation data. LST covered by the cloud under different conditions of surface solar radiation are simulated with the surface energy balance model. An interesting phenomenon is observed from the simulated result. The LST under the cloud decreases with minimal radiation that reaches the surface. The variation in radiation required for the LST change to 1 ℃ in unit time has a linear relationship with the LST value before cloud cover. The LST for cloud cover becomes a relatively stable value after 10–20 min under cloud cover. The LST covered by a rectangle with collective cloud parameters is simulated. The LST under cloud cover changes with the movement of the clouds. LST in front of the cloud along its moving direction is much higher than the middle and back area cover by the cloud after simulated with a hypothetical homogeneous rectangle cloud.关键词：remote sensing;land surface temperature;cloud;surface energy balance model;infrare
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发布时间：2021-06-07 - 摘要：Spaceborne repeat-pass InSAR is suitable for various elevation surveying and deformation measurements under all weather conditions. A satellite’s orbit is used to determine the interferometric baseline, which is an important index of interferometry performance. Result shows that the interferometric baseline of GF-3 satellite is longer than state-of-the-art repeat-pass InSAR systems overseas. However, its stability must be improved. This study aims to obtain a stable baseline that satisfies the requirements of repeat pass InSAR systems by analyzing the relative orbit elements and maneuver control. In this study, we use the first track of observation as the reference trajectory calculate the change law of repeat-pass baseline based on the relative orbit elements of repeated observations and reference orbits. Then, we analyze the baseline for targets under different latitudes. Based on the change law of baseline, we use the maneuver to adjust the relative orbit elements and develop a control method to achieve a baseline state. A repeat-pass InSAR baseline motion model was established on the basis of relative orbital elements to obtain a precise relative orbit. Real data were used to verify the model. The cross-baseline error between real data and the model was less than 10 m when the update frequency of orbit parameters was 1 Hz. We calculated the maneuver quantities based on relative orbital elements and formulated a control strategy for a special baseline control task. The change law of repeat pass InSAR baseline in global observation is analyzed, and the baseline length at different latitudes is obtained through the existing orbit control method, thus providing the basis for baselines when observing in different regions. The baseline length model is verified on the basis of actual data, and the actual baseline motion is determined in accordance with the model analysis. Then, an adjustment model based on the difference of relative orbital elements is established, and a control strategy from the predicted baseline to the required baseline is developed. Through the analysis of this study, the lengths of different observed baselines can be predicted with the number of known orbital elements, and the control strategy is adopted to adjust the observation baseline to satisfy the demand.关键词：repeat-pass InSAR;interferometry baseline;relative orbital elements;orbit control;GF-3
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发布时间：2021-06-07 - 摘要：Land Surface Temperature (LST), as a key parameter of surface physical processes at regional and global scales, is an important indicator of energy balance and climate change on the land surface. Land Surface Emissivity (LSE) is an intrinsic property of natural materials and is a key parameter of surface energy balance and mineral mapping. LSE is also an important input variable for LST retrieval. LST rapidly changes in space and time given the heterogeneity of land surface, and remote sensing in thermal infrared (TIR) provides a unique means of obtaining LST information at regional and global scales. GF-5 satellite, which was launched in 2018, is the fifth satellite in the national high-resolution Earth observation project of China. Visual and Infrared Multispectral Imager (VIMI) is a sensor onboard GF-5, which has four TIR channels centered at 8.20, 8.63, 10.80, and 11.95 μm with a spatial resolution of 40 m. In this study, we present a physics-based Temperature and Emissivity Separation (TES) algorithm (denoted as WVSTES algorithm) to retrieve LST and emissivity (LST&E) simultaneously from GF-5 VIMI data. The TES algorithm uses full radiative transfer simulations to isolate surface-emitted radiance and an emissivity calibration curve based on the variability in the surface radiance data to retrieve LST and spectral emissivity dynamically. Furthermore, an improved Water Vapor Scaling (WVS) model is adopted to improve the accuracy and stability of atmospheric correction for conditions with high atmospheric water vapor content. First, Seebor V5.0 atmospheric profile database and 81 emissivity spectra extracted from the ASTER spectral library were used to simulate WVS coefficients. Then, reanalysis data of the Modern Era Retrospective-analysis for Research and Applications (MERRA) and fast radiative transfer model RTTOV were used to perform the atmospheric correction of GF-5 TIR data. Second, atmospheric parameters, such as atmospheric transmittance, upwelling, and downwelling radiance, were adjusted through the WVS method. Finally, the LST&E were retrieved using the TES algorithm. Two methods were used to evaluate the accuracy of the proposed algorithm. The first method was used to evaluate the algorithm using simulated data constructed on Seebor profile database and MODTRAN 5.2 model. The second method was applied to validate the algorithm using 11 daytime simulated images from ASTER data acquired in the Heihe River Basin with the concurrent in situ LST&E measurements. First, the GF-5 at-sensor radiances were simulated using the MODTRAN 5.2 model with 9136 atmospheric profiles and 81 ASTER emissivity spectra to evaluate the simulated data. Second, the errors in atmospheric correction were simulated in terms of the total atmospheric water vapor content uncertainties, and the atmospheric profile was adjusted with a scaling factor of 1.2 in MODTRAN to simulate the errors. Finally, the standard TES and WVSTES algorithms were evaluated using the simulated data. Validation results using the simulated data show that LST RMSE reduces from 2.59 K to 1.54 K, and LSE RMSEs in the four bands reduce from 0.122, 0.12, 0.10, and 0.037 to 0.042, 0.040, 0.028, and 0.026, respectively, when the WVS model is applied. The validation results using the simulated GF-5 images show that the LST retrieved using the WVSTES algorithm agrees well with in situ LST data. The LST average bias reduces from 1.08 K to 0.47 K, and the RMSE reduces from 2.17 K to 1.70 K. The error between the retrieved emissivity and ground measured data is basically less than 1%. The abovementioned results indicate that the proposed WVSTES algorithm can retrieve accurate LST&E results, which can be used to obtain the LST&E with high accuracy and spatial resolution from the GF-5 VIMI data.关键词：remote sensing;land surface temperature;land surface emissivity;GF-5;WVS;TES
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发布时间：2021-06-07 - 摘要：Snow/ice surface albedo is an important climate parameter because it governs the surface energy budget on the earth–atmosphere system. MODIS snow/ice albedo products, which are generated on the basis of a kernel-driven Rossthick-LisparseR (RTLSR) model, are extensively used in quantitative remote sensing community. However, the model is developed on the basis of the physics of soil–vegetation system. In theory, the scattering properties between the snow/ice and soil/vegetation systems are distinctly different, thereby causing potential problems in characterizing a Bidirectional Reflectance Distribution Function (BRDF) and albedo for snow/ice cover type using this operational model. In this study, we assess the ability of the operational RTLSR model in characterizing the BRDF and retrieving the surface albedo of snow/ice using Polarization and Directionality of the Earth’s Reflectances (POLDER) BRDF data. To verify and compare BRDF/albedo for snow and ice using high-quality collected POLDER observations, we introduce an extensively used asymptotic radiative transfer (ART) model to estimate the BRDF/albedo surface with POLDER observations as a reference given the lack of in situ albedo observations. Results show that (1) the RTLSR model fits the POLDER observations with higher accuracy than the result derived using the ART model. The root mean square error (RMSE) reaches 0.0498 in the 1020 nm band, which increases by 53.70% in comparison with the result using the ART model. (2) The resultant albedos are significantly influenced by the inversion of the RTLSR model for the snow/ice cover type in comparison with the result using the ART model. The RMSE and bias between the two models reach 0.0333 and 0.0274, correspondingly, thus indicating a significant underestimation of surface albedos using the RTLSR model in comparison with the ART model. However, determination coefficient (R2) reaches 0.529, which indicates a high-level correlation between the RTLSR and ART models. Our findings show that the current operational RTLSR model cannot characterize the directional reflectance of snow/ice, thereby affecting the model’s capability in estimating the surface albedo for snow/ice, and thus, this model must be modified. Such development can improve the inversion accuracy of surface albedos when this model is used as an operational algorithm for snow/ice cover type in the future.关键词：POLDER;BRDF;RTLSR;ART;snow/ice reflectance;snow/ice albedo;model estimating
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发布时间：2021-06-07 -
摘要：Civilian earth observation satellite laser altimetry has developed rapidly in recent years. The ZY-3 02 satellite was launched and loaded as the first Chinese earth-observing satellite laser altimeter. The GF-7 satellite and terrestrial ecosystem carbon monitoring satellite with laser altimeter will be launched in the next several years. Analyzing the quality of national laser altimetry data is important to fill in the knowledge gap and boost the development of civilian laser altimetry technique. All the laser altimetry data in this study were processed using the rigorous geometric model. The data availability ratio and quality of the sea surface or during the side swing of the satellite was analyzed. The absolute elevation accuracy of laser footprint points was also evaluated using high-precision referenced terrain data. The availability ratio of ZY-3 02 laser altimetry data was approximately 30%. On the flat terrain, the quality and availability were relatively better than those of other terrains, and the absolute elevation accuracy could reach 1.0 m on flat terrains. The ZY-3 02 satellite laser altimeter can obtain efficient laser data during the on-orbit experiment, and the total availability is not high. However, it is relatively better at night and on flat regions. Intrinsic ranging error and attitude measurement error are the main sources of satellite laser elevation accuracy. The conclusion may be useful for the future national satellite laser altimeter.关键词：remote sensing;satellite laser altimetry;ZY-3 02;data processing;quality analysis;satellite side-scanning;night observation- 572
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发布时间：2021-06-07 -
摘要：Small target signals detected through remote sensing are typically weak signals. The traditional hyperspectral anomaly change detection method directly suppresses the background. However, it frequently causes small targets to be suppressed simultaneously, thereby resulting in a low target detection rate and a high false alarm rate. In this study, a hyperspectral anomaly change detection model based on independent component analysis is used. The proposed model is projected on an independent component, which first highlights the anomaly changes and then suppresses the background to achieve the effective separation of anomaly changes and background. This model can effectively reduce the false alarm rate and improve the detection rate. The accuracy is verified by simulation and real data. Results show that the detection accuracy is 99% using simulated data, and the detection accuracy is 86% using real data. The accuracy is increased by 9% in comparison with the traditional anomaly change detection algorithm. The proposed hyperspectral anomaly change detection method is suitable for processing weak targets. Remote sensing image change detection is the process of quantitatively analyzing the surface changes in remote sensing images that are not obtained in the same surface area simultaneously. However, change detection frequently fails to highlight the change in interest given the differences in atmospheric environment and radiation difference caused by various sensors. We aim to find the small changes that are rare and different from the overall background trend. Traditional hyperspectral anomaly detection methods are generally used to highlight abnormal changes by directly suppressing the background. However, the three methods mentioned above cannot effectively eliminate the radiation differences in the case of complex objects and cannot guarantee the consistency of the background. The background is difficult to suppress, and the anomaly changes cannot be highlighted. Abnormal change detection method based on Independent Component Analysis (ICA) through the abnormal changes in RX anomaly detection method of pixels selects the anomalies with strong changes in abnormal pixels for initial projection direction and to all pixels to initialize the orthogonal projection of projection direction and abnormal pixel labeling for a second projection direction until the number of iterations to achieve independent component. The visual discrimination results after joining LCRA anomaly change detection results effectively restrain the false alarm rates and highlight the anomaly change targets, and abnormal change detection obtains accurate results. The ICA results show that the accuracy of anomaly change detection is superior to other methods. The result shows the quantitative evaluation result of abnormal change detection. ICA realizes the highest accuracy. The ICA anomaly change detection method achieves a low false alarm rate and favorable detection effect, and this method can obtain the highest accuracy with or without the LCRA matching strategy. Considering the evident geometric matching error of real data, the accuracies of all methods improve by approximately 0.2 after the LCRA matching strategy is adopted. The result shows the corresponding accuracy of the ICA anomaly detection method for analyzing the number of different independent components. Moreover, the result accuracy of this method is the highest when the number of independent components is moderate and between 7 and 14, thereby reflecting its robustness. The accuracy of the proposed method is better than other methods when the parameter size is reasonable. This study presents an ICA model, which determines the abnormal changes in projection on an independent component and the prominent changes in the target and restrains the background to achieve the effective separation of target and background, effectively improve the detection rate, and achieve low false alarm rates. The conclusions are summarized as follows: (1)The proposed method that uses simulated data achieves abnormal change detection accuracy of 99% and real data detection accuracy of 86% in comparison with the traditional abnormal change detection algorithm, which accuracy reaches 9%; (2) Aiming at the abnormal change target of a subpixel level, the proposed method has a 1% improvement in accuracy in comparison with traditional abnormal change detection. (3) The proposed method has only one parameter, and the selection of parameters slightly impact accuracy, which has strong robustness.关键词：independent component analysis;anomaly change detection;projection pursuit;subpixel;remote sensing;hyperspectral- 560
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发布时间：2021-06-07 -
摘要：Air pollution processes in Northern China have been analyzed using Absorbing Aerosol Index (AAI) products and ground-based data, including Relative Humidity (RH) and Particulate Matter (PM) 2.5. The AAI data are obtained from the total ozone unit load on FY-3B, which is the second-generation polar orbiter of China. The correlation between AAI, which has a fairly high value, and PM2.5 is weak when RH has a high value. In this research, the relationship between satellite-retrieved AAI and RH is simulated. The effects of humidity on AAI are investigated under different aerosol types, such as urban and rural aerosols, using radiative transfer models Doubling–Adding KNMI (DAK) with consideration of the characteristics of the aerosol models of the low atmosphere and mode radii for the aerosol model as a function of RH. Rural aerosol consists of 30% dust-like aerosol and 70% water-soluble materials, including ammonium, calcium sulfate, and organic compounds. Urban aerosol consists of 80% rural aerosol and 20% carbonaceous aerosol-like soot caused by industrial emissions. The AAI increases with Aerosol Optical Depth (AOD) for the two aerosol models, whereas the AAI is higher and changes faster with AOD than with urban aerosol for the rural aerosol model. For the two aerosol models, the AAI remains constant with RH when the air is dry but changes rapidly when the air becomes humid, and the effects of RH on the AAI are opposite for rural and urban aerosols. Analysis results show that the AAI strongly depends on RH when absorbing aerosols, such as carbonaceous aerosol-like soot, exist in the atmosphere during air pollution, which frequently occurs in Northern China. This finding can well explain the huge discrepancy of the comparison between the AAI and ground-based measurements under high RH condition using the microphysical properties of aerosols. Under a dry condition and a certain AOD value, the AAI is larger in rural aerosol than in urban aerosol. This condition is due to more dust-like aerosols exist in the rural aerosol model than in the urban aerosol model, and dust-like aerosols have stronger absorbing ability than carbonaceous aerosols. Under the humid condition, dust-like aerosols in the rural model exert a weaker hygroscopic effect than carbonaceous aerosols caused by industrial emission in the urban model, whereas sulfate aerosols demonstrate a strong hygroscopic effect but only produce negative or zero AAI. Thus, the AAI of rural aerosol slightly decreases with the increase in RH. Soot-like aerosol, which only exists in urban aerosol, can grow large by taking a large amount of water in high RH ambient atmosphere and change the chemical composition of particles. Thus, the absorption ability enormously increases with RH. This condition makes RH have a stronger effect on the AAI in the urban aerosol model than in the rural aerosol model. The aerosol type and humidity must be considered important impacting factors when monitoring the AAI data derived from spaceborne UV instruments.关键词：remote sensing;Absorbing Aerosol Index;Relative Humility;DAK;PM2.5;FY-3/TOU- 545
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发布时间：2021-06-07 - 摘要：Airport detection is important for military and civilian applications. As a prominent feature of an airport, a runway is frequently used to recognize airport areas in image-processing fields. The classification method is extensively used to resolve the problem of airport detection in PolSAR images. That is, the runway area is extracted through the classification method to determine the airport area. A disadvantage in applying this method is that the number of class in the image must be manually set through the classification method. This process will decrease the robustness of the algorithm. Moreover, given that the classification is normally performed on the pixel of an image, a high computational cost is required. In this study, a fast, adaptive unsupervised classification method for runway detection is proposed. First, the coherent matrix of a PolSAR image is decomposed to construct the eigenvalue image, and then the superpixel image is obtained when the eigenvalue image is processed through the SLIC algorithm. Second, the number of categories of ground objects in the superpixel image is estimated, and the runway ROIs of the superpixel image are extracted through the spectral clustering method, which is an adaptive unsupervised classification. Finally, the prospected areas are further filtered morphologically, and the real runways are identified by combining with the structural features, such as the size, topology, and parallel characteristics of the runways. The algorithm is verified using multiple groups of real PolSAR images acquired through the UAVSAR system and compared with two representative unsupervised algorithms. Experimental results show that the proposed algorithm can detect the runway area quickly and accurately with low false alarm rate And that the runway structure is clear. These experimental results are improved with the use of the proposed algorithm. Thus, the following conclusions are drawn: The SLIC algorithm is used to form a superpixel image to compress the dimension of the original image and reduce computational cost. Simultaneously, speckle noise is suppressed slightly, This result is beneficial for the classification in the subsequent processing. The adaptive class number estimation of images with VAT and DBE algorithms increase the robustness of the proposed method. Runways are extracted effectively and accurately when the polarimetric feature of a PolSAR image is combined with the spectral clustering method. Further studies must be explored despite obtaining improved detection results through the proposed method. For example, improving the blurred part caused by a superpixel image in the detected airport area must be investigated. A probable scheme is to set the number of the superpixel of the image adaptively in accordance with the complexity of the original image contents to yield a high-quality superpixel image.关键词：remote sensing;PolSAR image;airport runway area detection;superpixel image;adaptive unsupervised classification;spectral clustering
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发布时间：2021-06-07 - 摘要：The core of building segmentation in high-resolution remote sensing image is to establish the mapping from an image feature space to a segmentation result with high dimension and strong nonlinearity. In a high-resolution remote sensing image, a building frequently emerges at any location in the entire image, thereby indicating that non-neighborhood pixels may be related to the current semantic segmentation pixel. The segmentation precision and generalization are significantly improved by adopting a Deep Neural Network (DNN) to extract the features and learn the nonlinear mapping in image segmentation. However, the non-neighborhood feature cannot be directly extracted by the DNN. This study presents an encoder–decoder deep learning architecture with ResNet and Conditional Random Field (CRF) for building semantic segmentation in a high-resolution remote sensing image to obtain high segmentation precision and reduce the obstacles from roads, staggered floors, and shadows. In the DNN, ResNet is used to establish the encoder for automatically extracting the building features, in which ResNet avoids the problems of vanishing and exploding gradient and accelerates the convergence of DNN weights. Before each convolution operation, batch normalization is adopted to normalize the sampling data and reduce the training difficulty of the DNN. Then, transposed convolution is applied to establish the decoder for reconstructing the image while segmenting the buildings. At the end of the DNN, the CRF is used to adjust the raw segmentation produced by the decoder. The value of a unary potential function in the CRF is given by the raw result of the decoder, and the pairwise potential function denotes the feature of pixel pairs in the entire image, which constructs a fully connected CRF (FCCRF). Considering that the calculation of FCCRF is considerable, a mean field algorithm is used to approximate the pairwise potential function value. Thus, convolution is used to obtain the pairwise potential function value, and a high-dimensional Gaussian filter is applied to implement the convolution operation. The mean field algorithm is implemented through an RNN mechanism. Thus, FCCRF becomes a part of the DNN, and the parameters of the CRF are trained with the encoder and decoder simultaneously. Experiments are conducted to validate the effectiveness of the proposed methodology. The remote sensing image dataset is Inria Aerial Image Labeling Dataset. A total of 4500 samples with 1000×1000×3 pixels are found in each sample, in which their resolution is 0.3 m. The typical kinds of building, such as building with order, single building with complicated roof, and building without order, are segmented through VGG, ResNet, and the proposed methodology (denoted as ResNetCRF), correspondingly. The results show that ResNetCRF overcomes the interruption of roads in which their color features are similar to the building and effectively reduces the disturbance of staggered floors and shadows. Thus, ResNetCRF obtains the optimal segmentation precision. The multi-resolution experiment demonstrates that ResNetCRF has a strong generalization under a limited range of resolution change. Accurate mapping of building segmentation is established to reduce the disturbance of roads, shadows, and staggered floors by introducing CRFs in the encoder–decoder based on ResNet to segment the building in a high-resolution remote sensing image. In the future work, we will investigate the reduction of FCCRF calculation, overcome the missing segmentation of small buildings, and reduce the segmentation errors of a building whose color feature is similar to the background without a noticeable edge.关键词：high resolution remote sensing image;deep neural network;conditional random fields;building segmentation
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发布时间：2021-06-07 -
摘要：As the northernmost and coldest area in Inner Mongolia with the largest wetland in China, high-resolution and full time–space-covered surface freeze/thaw (F/T) in Genhe area must be determined. Carbon–nitrogen cycle, water and soil losses, and soil freeze–thaw erosion must be investigated in the Genhe region. In this study, a downscaling method of passive microwave (PMW) brightness temperature (TB) proposed by Kou et al. is used to downscale the TB from 0.25° spatial resolution to 0.01° spatial resolution. The downscaled temperature data of 1 km spatial resolution produced by Zhang et al. are used. AMSR2 TB is adjusted to AMSR-E TB using a linear conversion model between different polarizations of AMSR2 and AMSR-E sensors. Then, PMW TB and downscaled TB data are adopted to discriminate the surface F/T status using the F/T discriminant function algorithms developed by Zhao et al. and Kou through model simulation and experimental data, correspondingly. A comparison between the F/T state and the soil temperature measured at 0–5 cm in Genhe area shows that the overall classification accuracy of the two F/T discriminant algorithms before and after downscaling is within 6.72%, and the accuracy difference is insignificant. The mean value of thawing classification accuracy is 10% higher in the F/T discriminant algorithm developed by Zhao et al. than in Kou’s algorithm (DFA_Kou). Kou’s freezing classification accuracy is faster than that of Zhao et al. (DFA_Zhao, approximately 1%). The freezing classification accuracies of the two F/T discriminant algorithms are higher than 90%, and the thawing classification accuracies during the ascending orbits are higher than 80%. The thawing classification accuracies of two algorithms during the derailment period range from 40%–82%. During the development of DFA_Zhao and DFA_Kou, the temperature data at 0–1 and 0 cm are introduced to correct the freeze–thawing discriminant obtained through simulation, thereby making the two algorithms sensitive to the freezing and thawing changes in surface soil. PMW has a certain depth of penetration, which may cause errors when using the two algorithms to monitor soil freeze–thaw conditions. The proposed downscaling method is based on the assumption that the land cover is basically the same during downscaling TB between the PMW large pixel (0.25°) before downscaling and small pixel after downscaling (0.01°). This method was proposed by Kou et al. (2017) in Naqu area on the plateau. The land cover of Naqu area is nearly entirely grassland. However, the land cover in Genhe area mainly consists of three types, namely, forest land, grassland, and agricultural land. The proposed method has limitations in mixed pixels of different surface types. Thus, under the premise of without introducing the experimental calibration data, the penetration depth of TB during freeze–thaw transformation and the body scattering effect of TB in the soil layer will be investigated in the next step. A high-resolution surface freeze–thaw monitoring algorithm suitable for complex surface types and full time–space coverage will be developed in the next step.关键词：AMSR2;surface freeze-thaw discrimination;downscaling;passive microwave;freeze-thaw discriminant algorithm- 610
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发布时间：2021-06-07 - 摘要：The greenhouse Gas Monitor Instrument (GMI) is a payload of GF-5 satellite. It is mainly used to ascertain the global distribution of carbon dioxide (CO2) and methane (CH4) and carbon cycling studies. Surface reflection is an important factor for developing a high-precision column-averaged carbon dioxide dry air mole fraction (XCO2) retrieval algorithm for the GMI short-wave infrared data. Urban areas are an important source of CO2 emission, and an apparent directional reflection occurs in the urban underlying surface. However, the aerosol optical depth (AOD 550 nm) in Urban Beijing is relatively large, and combining atmospheric scattering and surface directional reflection makes the retrieval of XCO2 in Beijing urban area difficult. In this study, we investigate the MODIS Bidirectional Reflectance Distribution Function (BRDF) data in Beijing urban area in 2011–2016. The three parameters (fiso, fgeo, and fvol) of the BRDF model vary with season. The ratio of fiso to fgeo and of fiso to fvol slightly change. Statistics show that the average value of fgeo/fiso is 0.42±0.09 and fvol/fiso is 0.23±0.03. The simplification of the BRDF model allows us to invert the BRDF parameters simultaneously through one observation of the GMI. We present a new algorithm to improve the accuracy of XCO2 in Beijing urban areas through simultaneous retrieval of BRDF parameters. An a priori value of AOD is provided by using a directional polarimetric camera, which is onboard the GF-5 satellite. The MODIS MCD43A1 product is used to build an a priori surface BRDF database after filtering and resampling. The a priori CO2 profile is estimated on the basis of the analysis of CO2 profile distribution in Beijing area. The monthly a priori covariance matrices of CO2 at a grid cell (2°×3°) on the globe is precalculated as input parameters using the carbon tracker database. The measurement error covariance matrix is (y/SNR)2, and the signal-to-noise ratio (SNR) used is the result of a laboratory test. This SNR is a tentative value and must be tuned after acquiring in-orbit data. To determine the effect of aerosols and surface BRDF reflection on the retrieval of XCO2, we simulate GMI measurements in the spectral range of 6310–6380 cm−1. We use absolute radiance, ratio radiance, and BRDF methods to retrieve the simulation data separately. The error of the absolute radiance method exceeds 30% when the AOD is 0.05 considering the changes in surface albedo caused by surface direction reflection, and the error of the ratio radiance method is less than 1% in most observations. By contrast, the BRDF method has high accuracy, and its error is less than 0.2%. In the case of high AOD (0.4), the maximum error of XCO2 is 4.75% without the inversion of BRDF parameters. The evaluated precisions of the retrieved XCO2 are less than 0.5% in most cases. To validate the correctness of our algorithm for the measured data, we retrieve the observations of the AOD less than 0.4 from Greenhouse Gases Observing Satellite (GOSAT) L2 data in Beijing urban area in 2016. We find that the correlation is 0.85, and the average error is 1.25 ppm by comparing our retrievals with GOSAT NIES XCO2. The retrievals are stable, except the results in summer. The AOD of 64.28% observations in summer is larger than 0.3, and the rest is close to 0.3. The difference in albedo caused by the BRDF characteristics of the urban underlying surface significantly influences the accuracy of XCO2 retrievals, and the high-precision CO2 concentration information cannot be obtained when it is not corrected. The aerosol conditions in urban areas are frequently complex. The proposed algorithm for the simultaneous retrieval of surface BRDF parameters and CO2 content can accurately describe the surface direction reflection. The proposed algorithm improves the retrieval accuracy and data utilization of GMI data in the urban areas of Beijing.关键词：GF-5;GMI;CO2 retrieval;BRDF;aerosol
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发布时间：2021-06-07 -
摘要：Leaf Area Index (LAI), which is defined as the single-sided leaf area per unit horizontal ground area, is a key biophysical variable in land surface processes related to vegetation dynamics, such as photosynthesis, transpiration, and energy balance. This study aims to validate the Multisource data Synergized Quantitative (MuSyQ) remote sensing production system LAI product, which is produced by multisource remote sensing data of China from 2010 to 2015, with 1 km spatial resolution and 5 day time step. Several performance criteria, such as continuity, spatial and temporal consistency, dynamic range of retrievals, statistical analysis per biome type, precision, and accuracy, were evaluated. In this study, the performance of the MuSyQ LAI product was assessed in two ways, namely, (1) by comparing the spatial and temporal characteristics of the MuSyQ LAI product with those of other moderate-resolution LAI products and (2) by comparing MuSyQ LAI values with ground measurement data. The spatial and temporal consistencies of the MuSyQ LAI product were evaluated through intercomparison with reference LAI products (MODIS C5, GLASS) over a network of homogeneous study sites (BELMANIP-2.1 and CERN) in China in 2010—2015. The accuracy of the MuSyQ LAI product was evaluated using numerous available ground reference maps. The MuSyQ LAI product presents a reliable and consistent spatial distribution of dynamic range in spatial distribution, thereby overcoming MODIS’ underestimation of the LAI values and the unreasonable spatiotemporal variations in MODIS LAI products. MuSyQ LAI and MODIS and GLASS LAI have the highest differences (systematic and total) and lowest correlations in evergreen broadleaf forest given the decrease in cloud or snow and other factors by comparing their spatial and temporal characteristics with other products. Qualitative analysis of temporal profiles shows that the MuSyQ LAI product exhibits a consistent seasonal variation with two other LAI products. The temporal profiles of MuSyQ LAI have a reasonable dynamic range and are smoother and more stable than MODIS. The main limitation of the MuSyQ LAI product is the existence of some inconsistencies in the time series at the forest site with evident fluctuations near the peak of the temporal profiles in comparison with the GLASS product. The high values of broadleaf crops during their growth season were slightly underestimated. Comparisons with LAI surface measurements show that the MuSyQ product agrees well with the LAI data (highest correlation, lower systematic error, and overall error) and is slightly underestimated at high values in the broadleaf growing season, where several broadleaf forest sites were slightly overestimated. In summary, the MuSyQ LAI product based on multisource remote sensing data immensely improves the product time resolution to 5 days and the spatiotemporal continuity of LAI products. The MuSyQ LAI product exhibits favorable performance, where the spatial distribution in China is realistic, and the spatial and temporal continuity is greater than other similar products. The temporal profiles reflect the seasonal characteristics of vegetation well, with a slight difference from the ground reference measurement values. The improvement in the spatial and temporal continuity is helpful for applying LAI in dynamic models (such as crop growth and surface process models). However, these validation results are limited by the ground dataset used to quantify the uncertainties attached to the satellite products. Currently, limited data on broadleaf evergreen, deciduous forests, and large regions around the world (e.g., Asia) are available. Their representativeness can be improved by obtaining multitemporal measurements on various sites, thereby sampling the various biome types found around the world. Furthermore, the uncertainties associated with ground reference data must be quantified to provide a reliable analysis of the validation results.关键词：remote sensing;Leaf Area Index(LAI);MuSyQ;multi-sensor;validation;MODIS;GLASS;China- 655
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发布时间：2021-06-07 -
摘要：Remote sensing has been recognized as an effective tool for monitoring water quality in inland waters, especially in algal lakes. However, water quality in macrophytic lakes where aquatic vegetation grows because of mixed pixels is difficult to retrieve. In this study, a classification retrieval method and image data acquired between July 2014 and June 2015 using GF-1 WFV and HJ-1A/1B CCD sensors were proposed to monitor Total Suspended Matter (TSM) and turbidity in a macrophytic lake, namely, the Weishan Lake, considering aquatic vegetation phenology. In the classification retrieval method, the Weishan Lake was divided into water overlying aquatic vegetation and water area using normalized difference water index. First, a qualitative method was proposed to retrieve TSM concentration and turbidity in water overlying aquatic vegetation considering aquatic vegetation phenology. In the qualitative method, time series MODIS NDVI data were used to obtain the time–spectrum curves of aquatic vegetation in the Weishan Lake for identifying different aquatic vegetation phenological periods. The characteristics of aquatic vegetation in different phenological periods were used to estimate the TSM concentration and turbidity. Second, single band, band ratio, and partial least squares models were applied to retrieve the TSM concentration and turbidity in the water area. Finally, the temporal and spatial variations in the TSM concentration and turbidity along with aquatic vegetation in the entire Weishan Lake were analyzed. Results showed that the three main aquatic vegetation areas in the Weishan Lake, namely,Potamogeton lucens, Myriophyllum spicatum, and Potamogeton crispus, have different phenological periods. P. lucens and M. spicatum started to grow in spring, reached their peak at the end of summer, and gradually died in autumn. P. crispus started to grow in April, reached its peak at the end of spring, and quickly died in the early summer. Different aquatic vegetation areas had various indicators of TSM concentration and turbidity in different phenological periods. The TSM concentration was less than 15 mg/L in the water overlying three aquatic vegetation areas in the growth stage. The turbidity in the water overlyingP. lucens, M. spicatum, and P. crispus in the growth stage were less than 30 and 15 NTU. The death of P. lucens and M. spicatum did not result in the deterioration of water quality because of their extensive death time. However, the death of P. crispus within a short time resulted in serious deteriorations of water quality. The TSM concentration and turbidity were 15–145 mg/L and 30–140 NTU, correspondingly, in the water overlying P. crispus in the death stage. The TSM concentration and turbidity in the Weishan Lake had a significant temporal–spatial variability. The TSM concentration and turbidity in the Southwestern Weishan Lake where P. lucens and M. spicatum grew had low levels in the seasons. By contrast, the TSM concentration and turbidity in the Northeastern Weishan Lake with P. crispus growth had low levels in spring. However, they became large in summer because of the quick death of P. crispus and gradually decreased in autumn and winter. A classification retrieval method coupled with quantitative monitoring in the water overlying aquatic vegetation and qualitative monitoring in the water area was proposed to monitor the TSM concentration and turbidity in the Weishan Lake considering aquatic vegetation phenology. This method effectively monitored the temporal and spatial variations in water quality in the entire Weishan Lake. The findings indicated that the proposed method can be used to monitor the water quality in other macrophytic lakes.关键词：remote sensing;macrophytic lake;aquatic vegetation;time-spectrum curve;phenology;remote sensing monitoring;GF-1;HT-1- 710
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发布时间：2021-06-07 -
摘要：Extraction of individual tree information is significant in managing forestry resources and protecting the ecological environment. In addition, this strategy has been extensively applied to forests using high spatial resolution satellite images or Lidar data in recent years. The rapid development of unmanned aerial vehicle remote sensing makes itself play an important role in this field. This study uses high spatial resolution aerial remote sensing images to extract individual tree information automatically. The individual tree information includes individual tree extraction and crown delineation. A novel extraction method of individual tree information was proposed in this study. First, the spectral information of the original images was enhanced through decorrelation stretch. Spectral information enhancement aimed to expand the coupling degree of image information by stretching the principal component information of the bands that are correlated, thus increasing the color saturation of the image. Second, the optimal cluster number of K-means clustering algorithm was obtained by introducing a DBI index, then the image pixels were marked based on the K-means clustering algorithm. On this basis, a GMRF model was constructed for image segmentation. Finally, the segmentation results were post-processed through mathematical morphology to complete individual tree crown delineation, and individual tree detection was based on the position of individual tree calculated by image geometric moment. This study acquired the reference data through visual interpretation to evaluate the result. The verification result showed that the overall accuracies of individual tree detection through this method are 89.52% and 95.65% for medium and low canopy density forests, correspondingly. The accuracies of individual tree crown delineation achieved 81.90% and 95.65% for medium and low canopy density forests, respectively. Therefore, the proposed method is better than the object-oriented method in terms of extraction effect through comparison and analysis. The proposed method can be used to obtain the individual tree information of forestry management rapidly and has high extracting precision based on the verification of results. The proposed method does not require considerable manual intervention and prior knowledge, thus significantly improving the degrees of automation.关键词：remote sensing;individual tree detection;individual tree crown delineation;Pinus sylvestris;Pinus tabulaeform;Unmanned Aerial Vehicle (UAV) remote sensing;GMRF- 753
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发布时间：2021-06-07
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